| dc.description.abstract | Intrusion detection is an action to monitor and analyze network traffic. In conducting
monitoring and analysis, intrusion detection system should have early detection
against intrusion attacks. This research uses HGN scheme to classify patterns of
intrusion attacks that exist in the KDD Cup 99 dataset. This research is performed in
two stages: preprocessing stage that includes the selection attributes using Information
Gain Attribute Evaluation techniques, discretization using Entropy Based
Discretization Supervised methods, election of training data using K-Means clustering
algorithm, and processing stage as a classification process using scheme HGN. The
results of the classification process is used to measure the accuracy rate, detection
rate, false positive rate and true negative rate. The test result shows that the HGN
scheme is very good and stable in classifying the intrusion attack patterns with
accuracy rate reaches 96.27%, detection rate reaches 99.20%, true negative rate below
15.73%, while false positive rate is very low with a percentage of 0.80%. | en_US |
